Towards equitable and immersive outdoor orienteering: An artificial intelligence-driven multi-objective route planning framework with augmented sand cat swarm optimization
Qingzhu Lun, Boya Li, Yuehui Zhou
TL;DR
This paper introduces an AI-powered route planning framework for outdoor orienteering that balances fairness and user experience using a new optimization algorithm.
Contribution
A novel multi-objective route planning framework with an enhanced sand cat swarm optimization algorithm for equitable and immersive orienteering.
Findings
The framework demonstrates consistent performance improvements in route optimality metrics.
It balances competitive equity and user experience across diverse terrain profiles.
The enhanced SCSO algorithm efficiently solves complex route design challenges.
Abstract
Outdoor orienteering has emerged as a globally popular recreational activity and competitive sport, combining navigational challenges with physical endurance across diverse natural terrains. Despite its growing popularity, the design of optimal orienteering routes presents significant challenges for recreation planners, requiring careful consideration of both competitive fairness and participant engagement. To address these challenges, this study establishes five fundamental design principles that systematically balance competitive equity with user experience enhancement. Building upon these principles, we develop a novel computational framework that integrates mathematical modeling techniques with intelligent optimization algorithms. Specifically, our methodology reformulates the route design challenge as a constrained multi-objective optimization problem and introduces an enhanced…
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Taxonomy
TopicsAdventure Sports and Sensation Seeking · Winter Sports Injuries and Performance · Outdoor and Experiential Education